Compute TALE and domain relatedness by domain-sequence alignment (DisTAL)

Quantifies how TALE arrays, and the individual domains they are built from – repeats and the two termini alike – relate to one another by aligning their domain sequences. An R re-implementation of the original DisTAL Perl program, including the array alignment step (the ARLEM model, see tales_tale_distances), with parallel domain alignment, which makes it much faster (the exact speedup depends on aln_method).

Usage

tales_compare_distal(
  x,
  ncores = 1,
  aln_method = "DECIPHER",
  conda_bin = "auto"
)

Arguments

x

A tales object whose parts carry amino acid sequences.

ncores

Number of cores for the pairwise alignment step.

aln_method

Approach for pairwise distances between part amino acid sequences: "DECIPHER" (default), "Biostrings" or "mmseq2". See tales_domain_distances.

conda_bin

Path to a Conda binary, if reticulate cannot find it.

Value

A list of three objects, all describing the same run:

• tales: the input, with a dom_code column added.

• domain_distances: a domain_distances between distinct domains, keyed by dom_code.

• tale_distances: a tale_distances between whole arrays, keyed by array_id.

Details

The name says which comparison this is: tales_compare_functal compares TALEs by their predicted DNA-binding specificity instead.

It returns the two expensive products (the pairwise protein alignment between distinct domains, and the alignment of the coded arrays) with the coded input; anything else is cheap to derive from these.

This is where dom_code is minted, over the whole set of parts supplied, and where tales/domain_distances are stamped with a namespace identifying that set — see tales_namespace. Passing a subset later is safe; re-running on a different part set mints different codes, and the differing namespace is what stops the two being joined by mistake. tale_distances carries no such stamp: it is keyed by array_id, a name that keeps its meaning across runs.

References

Pérez-Quintero A.L. et al. (2015). QueTAL: a suite of tools to classify and compare TAL effectors functionally and phylogenetically. Frontiers in Plant Science 6, 545. doi:10.3389/fpls.2015.00545

Abouelhoda M.I., Giegerich R., Behzadi B., Steyaert J.-M. (2009). Alignment of minisatellite maps based on run-length encoding scheme. Journal of Bioinformatics and Computational Biology 7(2), 287–308. doi:10.1142/S0219720009004060

See also

tales_group_hclust/tales_group_kmedoids to cluster arrays from the returned tale_distances; tales_compare_functal, comparing TALEs by predicted DNA-binding specificity instead of domain-sequence relatedness.

Other pairwise distances: [.pairwise_distances(), as.matrix.pairwise_distances(), distances_assert_square(), distances_restrict(), is_pairwise_distances(), pairwise_distances(), rvd_dna_specificity, tales_assign_domain_codes(), tales_compare_functal(), tales_domain_distances(), tales_group_hclust(), tales_group_kmedoids(), tales_tale_distances(), validate_pairwise_distances()

Examples

x <- tales_from_telltales(system.file("extdata", "tellTaleExampleOutput",
                                      package = "tantale"))
cmp <- tales_compare_distal(x)
#> Computing a distance matrix between TALE parts amino acid sequences using:
#> DECIPHER
#> Deriving domain substitution costs that meet the triangle inequality (Minkowski
#> distance between domain distance profiles).
#> Aligning 4 TALE arrays pairwise (6 pairs).
#> Finished computing TALE and repeat relatedness.
names(cmp)
#> [1] "tales"            "domain_distances" "tale_distances"  
cmp$tale_distances
#> # A tibble: 16 × 5
#>    id1       id2       dissim arlem_score max_length
#>    <chr>     <chr>      <dbl>       <dbl>      <int>
#>  1 ROI_00001 ROI_00001   0              0         28
#>  2 ROI_00002 ROI_00001   5.64         158         28
#>  3 ROI_00003 ROI_00001   4.96         139         28
#>  4 ROI_00004 ROI_00001   5.29         148         28
#>  5 ROI_00001 ROI_00002   5.64         158         28
#>  6 ROI_00002 ROI_00002   0              0         16
#>  7 ROI_00003 ROI_00002   6            168         28
#>  8 ROI_00004 ROI_00002   5.62         135         24
#>  9 ROI_00001 ROI_00003   4.96         139         28
#> 10 ROI_00002 ROI_00003   6            168         28
#> 11 ROI_00003 ROI_00003   0              0         28
#> 12 ROI_00004 ROI_00003   5.43         152         28
#> 13 ROI_00001 ROI_00004   5.29         148         28
#> 14 ROI_00002 ROI_00004   5.62         135         24
#> 15 ROI_00003 ROI_00004   5.43         152         28
#> 16 ROI_00004 ROI_00004   0              0         24